Example- Schedule Optimization
Summarize
Summary of Example- Schedule Optimization
This example demonstrates how ServiceNow admins can configure the Schedule Optimization engine to efficiently schedule tasks for agents. It highlights three configuration methods: running batch optimizations overnight for large task volumes, executing intraday optimizations at selected intervals based on events, and enabling dispatchers to trigger on-demand optimizations directly from the Dispatcher Workspace. The goal is to maximize task assignments while minimizing travel time during agent shifts.
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Key Features
- Batch Optimization: Configured to run overnight weekly batches (e.g., from 10 PM to 3 AM) to schedule a large number of tasks over a defined assignment horizon.
- Intraday Optimization: Runs during business hours (e.g., 9 AM to 5 PM) to adjust schedules throughout the day based on current task and agent status.
- On-Demand Optimization: Allows dispatchers to manually initiate scheduling optimizations in real time via Dispatcher Workspace for immediate adjustments.
- Policy Configuration: Policies are set to maximize assignments, minimize travel time, and prioritize earlier shifts with equal weighting.
- Scheduling Attributes: Includes travel time estimation using Beans.ai and specifies task states eligible for scheduling (Pending dispatch or Scheduled).
- Scope and Assignment Groups: Defines geographic scopes (e.g., West Coast regions) and assignment groups (San Diego North and South) for targeted scheduling.
Key Outcomes
- Admins can balance workload and travel efficiency by applying policies that prioritize maximizing assignments and minimizing travel time.
- Batch scheduling enables handling large task volumes efficiently during off-hours, improving resource utilization.
- Intraday and on-demand scheduling provide flexibility to react to dynamic operational changes during the workday.
- Dispatchers gain control to initiate immediate optimizations, ensuring agents' schedules remain optimal as conditions change.
- The configuration tables serve as practical references for setting scheduling resolutions, batch timings, policies, and enabling on-demand features per assignment group.
This example shows three different ways admins can configure the optimization engine to schedule tasks.
Admins can configure Schedule Optimization to run overnight in batches to schedule a larger number of tasks or throughout the day at selected intervals based on events. Admins can also enable dispatchers to initiate Schedule Optimization from Dispatcher Workspace by configuring on-demand optimization.
In this example, the organization is ensuring that agents complete as many tasks as they can during their shift without spending a lot of time traveling between tasks. A policy is configured to maximize assignments and minimize travel time. On-demand optimization is enabled for the dispatchers who are assigned to this group of agents.
Admin Core Configurations for Schedule Optimization
| Field | Value |
|---|---|
| Qualifier type for Schedule Optimization | Assignment Group |
| Number of seconds used for task scheduling resolution | 1 |
| Maximum number of location points allowed in a map provider call | 300 |
| Field | Value |
|---|---|
| Name | Maximum Assignments |
| Active | true |
| Constraints | Default values |
| Overall objectives | Maximize travel time (weight 1) Maximize task assignments (weight 1) Maximize assignments to earlier shifts (weight 1) |
| Field | Value |
|---|---|
| Name | West coast config |
| Active | True |
| Travel estimate provider | Beans.ai |
| Default policy | Maximum Assignments |
| Straight line estimate config | West Coast |
| Tasks | State is one of: Pending dispatch or Scheduled |
| On Demand applicable policy | West Coast Dispatcher |
Batch Optimization Configurations
| Field | Value |
|---|---|
| Name | West Coast weekly |
| Schedule start date | 2023-12-01 |
| Run frequency | Every 7 days |
| Batch start time | 22:00 |
| Batch end time | 3:00 |
| Field | Value |
|---|---|
| Name | West Coast-Next 7 days |
| Active | True |
| Scheduling attribute configuration | West Coast config |
| Rank | 1 |
| Assignment horizon offset | 00 |
| Assignment horizon range | Days 7 |
| Optimization Batch | West Coast weekly |
| Start date | 2023-12-01 |
| Batch start time | 22:00 |
| Batch end time | 3:00 |
| Assignment group | San Diego North |
Intraday Optimization Configurations
| Field | Value |
|---|---|
| Name | West Coast |
| Active | True |
| Default scheduling attribute configuration | West Coast config |
| Default | False |
| Flow | Schedule intraday jobs (default) |
| Default processing window | Workday 9:00-5:00 |
| Assignment group | San Diego South - Enable On Demand = True San Diego North - Enable On Demand = True |
On-demand Optimization configurations
| Field | Value |
|---|---|
| On Demand applicable policy | West Coast Dispatcher |
| Field | Value |
|---|---|
| Assignment group | San Diego South - Enable On Demand = True San Diego North - Enable On Demand = True |